Cost‐effectiveness analysis of community‐led HIV self‐testing among key populations in Côte d'Ivoire, Mali, and Senegal

ABSTRACT Introduction HIV self‐testing (HIVST) is a promising strategy to improve diagnosis coverage among key populations (KP). The ATLAS (Auto Test VIH, Libre d'Accéder à la connaissance de son Statut) programme implemented HIVST in three West African countries, distributing over 380,000 kits up between 2019 and 2021, focussing on community‐led distribution by KP to their peers and subsequent secondary distribution to their partners and clients. We aim to evaluate the cost‐effectiveness of community‐led HIVST in Côte d'Ivoire, Mali and Senegal. Methods An HIV transmission dynamics model was adapted and calibrated to country‐specific epidemiological data and used to predict the impact of HIVST. We considered the distribution of HIVST among two KP—female sex workers (FSW), and men who have sex with men (MSM)—and their sexual partners and clients. We compared the cost‐effectiveness of two scenarios against a counterfactual without HIVST over a 20‐year horizon (2019–2039). The ATLAS‐only scenario mimicked the 2‐year implemented ATLAS programme, whereas the ATLAS‐scale‐up scenario achieved 95% coverage of HIVST distribution among FSW and MSM by 2025 onwards. The primary outcome is the number of disability‐adjusted life‐years (DALY) averted. Scenarios were compared using incremental cost‐effectiveness ratios (ICERs). Costing was performed using a healthcare provider's perspective. Costs were discounted at 4%, converted to $USD 2022 and estimated using a cost‐function to accommodate economies of scale. Results The ATLAS‐only scenario was highly cost‐effective over 20 years, even at low willingness‐to‐pay thresholds. The median ICERs were $126 ($88–$210) per DALY averted in Côte d'Ivoire, $92 ($88–$210) in Mali and 27$ ($88–$210) in Senegal. Scaling‐up the ATLAS programme would also be cost‐effective, and substantial epidemiological impacts would be achieved. The ICERs for the scale‐up scenario were $199 ($122–$338) per DALY averted in Côte d'Ivoire, $224 ($118–$415) in Mali and $61 ($18–$128) in Senegal. Conclusions Both the implemented and the potential scale‐up of community‐led HIVST programmes in West Africa, where KP are important to overall transmission dynamics, have the potential to be highly cost‐effective, as compared to a scenario without HIVST. These findings support the scale‐up of community‐led HIVST to reach populations that otherwise may not access conventional testing services.

0% negative ICER 66 (20 -140) *ICER can be negative if the HIV self-testing (HIVST) distribution is leading to more disability-adjusted life years (DALY).This can occur if linkage to confirmatory testing after a reactive HIVST is low and there is test substitution.The estimates are reported based on the median and the 90% Uncertainty Interval provided for simulations in which the ICER are positives.

Topic
No. Item Location where item is reported Title 1 Identify the study as an economic evaluation and specify the interventions

Background and objectives 3
Give the context for the study, the study question, and its practical relevance for decision making in policy or practice.

Health economic analysis plan
4 Indicate whether a health economic analysis plan was developed and where available.

Study population 5
Describe characteristics of the study population (such as age range, demographics, socioeconomic, or clinical characteristics).

Setting and location 6
Provide relevant contextual information that may influence findings.

Comparators 7
Describe the interventions or strategies being compared and why chosen.

Perspective 8
State the perspective(s) adopted by the study and why chosen.

Time horizon 9
State the time horizon for the study and why appropriate.

Discount rate
10 Report the discount rate(s) and reason chosen.

Selection of outcomes
11 Describe what outcomes were used as the measure(s) of benefit(s) and harm(s).

Measurement of outcomes
12 Describe how outcomes used to capture benefit(s) and harm(s) were measured.

Valuation of outcomes
13 Describe the population and methods used to measure and value outcomes.

Measurement and valuation of resources and costs
14 Describe how costs were valued.Methods

Currency, price date, and conversion
15 Report the dates of the estimated resource quantities and unit costs, plus the currency and year of conversion.

Rationale and description of model
16 If modelling is used, describe in detail and why used.Report if the model is publicly available and where it can be accessed.

Analytics and assumptions
17 Describe any methods for analyzing or statistically transforming data, any extrapolation methods, and approaches for validating any model used.

Characterizing heterogeneity
18 Describe any methods used for estimating how the results of the study vary for subgroups.

Summary of main results
23 Report the mean values for the main categories of costs and outcomes of interest and summarise them in the most appropriate overall measure.

Côte d'Ivoire
Modelled epidemiology of the counterfactual no HIV self-test scenario        and c) men who have sex with men (MSM) LHIV show median projections and 90% uncertainty interval (UI) with black lines and grey shading.Red markers display local survey data with 95% confidence intervals [15].Estimates while grey indicates sexually transmitted infections (STI) clinic estimates [11], considered overestimates and excluded from model fitting but provided for comparison.

Figure S1b .
Figure S1b.HIV prevalence and impact of HIV self-testing (HIVST) in Côte d'Ivoire for adults over 15 years old as compared to UNAIDS 2018 estimates [6, 7], covering a) overall prevalence, b) incidence rates, c) new HIV acquisitions, and d) deaths annually.Black lines and grey shades indicate median and 90% uncertainty intervals (UI); red points and intervals for empirical data and 95% confidence intervals (95%CI); dark points in panel a) for UNAIDS comparisons, and grey points in panel c) show new UNAIDS estimates from July 2023, not available at the time of our original analysis.

Figure S1c .
Figure S1c.Model fits and projections of HIV prevalence in Côte d'Ivoire for a) female sex workers (FSW), b) clients of FSW, c) men who have sex with men (MSM), d) MSM aged 15-24, and e) MSM aged 25-49, using various sources [8-21].Median projections and 90% uncertainty interval (UI) are shown in black lines and grey shading; red points for empirical data.Yellow points in panel c) show aggregate estimates from studies among MSM reporting prevalence data for men who have sex with men and women (MSMW) and men who have sex with men exclusively (MSME) separately (not shown), which were both fitted.

Figure S1d .
Figure S1d.Projections and model fits in Côte d'Ivoire for the percentage of a) females and b) males people living with HIV (PLHIV) who are diagnosed.Median values and 90% UI are depicted with black lines and grey shading; red points indicate UNAIDS Shiny90 data used for calibration [22].

Figure S1e .
Figure S1e.Model fits and projections in Côte d'Ivoire for the proportion of people living with HIV (PLHIV) diagnosed among a) female sex workers (FSW), b) male clients of FSW,and c) MSM[13,15,19,20,23,24]. Median and 90% uncertainty levels are shown with black lines and grey shades; red markers indicate empirical survey-based estimates (95% confidence intervals), which are possibly underestimated[25].

Figure S1f .
Figure S1f.Model projections and fits in Côte d'Ivoire for antiretroviral treatment (ART) coverage among all people living with HIV (PLHIV) aged 15-59, categorized by sex: a) females and b) males.Median and 90% uncertainty interval (UI) are illustrated with black lines and grey shading; red marking show UNAIDS estimates (95% confidence intervals) via the Spectrum/EPP model [26].

Figure S1g .
Figure S1g.Model fits and projections in Côte d'Ivoire for antiretroviral treatment (ART) coverage in a) female sex worker (FSW) living with HIV(LHIV), b) male clients of FSW LHIV, and c) men who have sex with men (MSM) LHIV show median projections and 90% uncertainty interval (UI) with black lines and grey shading.Red markers display local survey data with 95% confidence intervals[15].Estimates while grey indicates sexually transmitted infections (STI) clinic estimates[11], considered overestimates and excluded from model fitting but provided for comparison.

Figure S1h .
Figure S1h.Model fits and projections in Côte d'Ivoire for HIV viral load suppression (VLS) among a) females, b) males, c) female sex workers (FSW) [15], and d) men who have sex with men (MSM) aged 15-49 [24] with HIV, showing median and 90% uncertainty intervals with black lines and grey shading.Red points are based on local surveys.

Figure S1i .
Figure S1i.Côte d'Ivoire model fits and projections for the percentage of viral suppression among people living with HIV (PLHIV) on antiretroviral treatment (ART), for both a) females and b) males, as per the third UNAIDS "95%" goal.Median projections and 90% uncertainty intervals are depicted with black lines and grey shading, while red markers show UNAIDS parameters[26].A grey dashed line marks the UNAIDS 2025 target of 95% viral suppression in those on ART.

Figure S2a .
Figure S2a.Modelled percentage of undiagnosed people living with HIV over time in Côte d'Ivoire from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex worker (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIV self-testing (HIVST) scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.

Figure S2b .Figure S3a .
Figure S2b.Modelled proportion of people treated for HIV over time in Côte d'Ivoire from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex workers (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIV self-testing (HIVST) scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.

Figure S3b .
Figure S3b.HIV prevalence and impact in Mali for adults over 15 years old as compared to UNAIDS estimates[7], covering a) overall prevalence, incidence rates, c) new HIV acquisitions, and d) deaths annually.Black lines and grey shades indicate median and 90% uncertainty intervals; red points and intervals for empirical data and 95% confidence intervals[26]; dark points in panel a) for UNAIDS comparisons.

Figure S3c .
Figure S3c.Model fits and projections of HIV prevalence in Mali for a) female sex workers (FSW), b) clients of FSW, c) men who have sex with men (MSM), d) MSM aged 15-24, and e) MSM aged 25-49, using various sources [30-41], Median projections and 90% uncertainty intervals are shown in black lines and grey shading; red points for empirical data.The yellow point in panel c) shows an aggregate estimate from a study among MSM which reported prevalence data for men who have sex with men and women (MSMW) and men who have sex with men exclusively (MSME) separately (not shown), which were both fitted.

Figure S3d .
Figure S3d.Projections and model fits in Mali for the percentage of a) females and b) males people living with HIV (PLHIV) who are diagnosed.Median values and 90% uncertainty intervals are depicted with black lines and grey shading; red points indicate UNAIDS Shiny90 data used for calibration [22].

Figure S3e .
Figure S3e.Model fits and projections in Mali for the proportion of people living with HIV (PLHIV) diagnosed among a) female sex workers (FSW), b) male clients of FSW, and c) men who have sex with men (MSM) [40].Median and 90% uncertainty levels are shown with black lines and grey shades; red markers indicate empirical survey-based estimates (95% confidence intervals), which are possibly underestimated [25].

Figure S3f .
Figure S3f.Model projections and fits in Mali for antiretroviral treatment (ART) coverage among all people living with HIV (PLHIV) aged 15-59, categorized by sex: a) females and b) males.Median and 90% uncertainty intervals are illustrated with black lines and grey shading; red marking show UNAIDS estimates (95% confidence intervals) via the Spectrum/EPP model [26].

Figure S3g .
Figure S3g.Model fits and projections in Mali for antiretroviral treatment (ART) coverage in a) female sex workers (FSW) living with HIV(LHIV), b) male clients of FSW LHIV,and c) men who have sex with men LHIV show median projections and 90% uncertainty intervals with black lines and grey shading[40].Black point and interval display self-reported data with 95% confidence intervals.

Figure S3h .
Figure S3h.Model fits and projections in Mali for HIV viral load suppression (VLS) among a) females, b) males, c) female sex workers (FSW) [37], and d) men who have sex with men (MSM) aged 15-49 with HIV [40, 41], showing median and 90% uncertainty intervals with black lines and grey shading.Red points are based on local surveys.

Figure S3i .
Figure S3i.Mali model fits and projections for the percentage of viral suppression among people living with HIV (PLHIV) on antiretroviral treatment (ART), for both a) females and b) males, as per the third UNAIDS "95%" goal[26].Median projections and 90% uncertainty intervals are depicted with black lines and grey shading.A grey dashed line marks the UNAIDS 2025 target of 95% viral suppression in those on ART.

Figure S4a .
Figure S4a.Modelled proportion of undiagnosed people living with HIV over time in Mali from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex workers (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIVST scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.

Figure S4b .Figure S5a .
Figure S4b.Modelled proportion of people treated for HIV over time in Mali from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex workers (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIVST scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.

Figure S5b .
Figure S5b.HIV prevalence and impact in Senegal for adults over 15 years old analyzed as compared to UNAIDS estimates[7], covering a) overall prevalence, b) incidence rates, c) new HIV acquisition, and d) deaths annually.Black lines and grey shades indicate median and 90% uncertainty intervals; red points and intervals for empirical data and 95% confidence intervals[26]; dark points in panel a) for UNAIDS comparisons.

Figure S5c .
Figure S5c.Model fits and projections of HIV prevalence in Senegal for a) female sex workers (FSW), b) clients of FSW, c) men who have sex with men (MSM), d) MSM aged 15-24, and e) MSM aged 25-49, using various sources [44-56].Median projections and 90% uncertainty intervals are shown in black lines and grey shading; red points for empirical data.Yellow points in panel c) show aggregate estimates from studies among MSM which reported prevalence data for men who have sex with men and women (MSMW) and men who have sex with men exclusively (MSME) separately (not shown), which were both fitted.

Figure S5d .
Figure S5d.Projections and model fits in Senegal for the percentage of a) females and b) males people living with HIV (PLHIV) who are diagnosed.Median values and 90% uncertainty intervals are depicted with black lines and grey shading; red points indicate UNAIDS Shiny90 data used for calibration [22].

Figure S5e .
Figure S5e.Model fits and projections in Senegal for the proportion of people living with HIV (PLHIV) diagnosed among a) female sex workers (FSW), b) male clients of FSW, and c) men who have sex with men (MSM) [38, 48, 50, 54, 57-60].Median and 90% uncertainty intervals are shown with black lines and grey shades; red markers indicate empirical survey-based estimates (95% CI), and the black marker in a) represent estimates from a sexually transmitted infections (STI) clinic.Both are possibly underestimated [25].

Figure S5f .
Figure S5f.Model projections and fits in Senegal for antiretroviral treatment (ART) coverage among all people living with HIV (PLHIV) aged 15-59, categorized by sex: a) females and b) males.Median and 90% uncertainty intervals are illustrated with black lines and grey shading; red marking show UNAIDS estimates (95% confidence intervals) from the Spectrum/EPP model [26].

Figure S5g .
Figure S5g.Model fits and projections in Senegal for antiretroviral treatment (ART) coverage in a) female sex workers (FSW) living with HIV(LHIV), b) male clients of FSW LHIV, and c) men who have sex with men (MSM) LHIV show median projections and 90% uncertainty intervals with black lines and grey shading.Red marker represents empirical estimates from local surveys.Black points and intervals display self-reported data with 95% confidence intervals[50].

Figure S5h .
Figure S5h.Model fits and projections in Senegal for HIV viral load suppression (VLS) among a) females, b) males, c) female sex workers (FSW), and d) men who have sex with men (MSM) aged 15-49 with HIV showing median and 90% uncertainty intervals with black lines and grey shading.Red points are based on local surveys [52].

Figure S5i .
Figure S5i.Senegal model fits and projections for the percentage of viral suppression among people living with HIV (PLHIV) on antiretroviral treatment (ART), for both a) females and b) males, as per the third UNAIDS "95%" goal[26].Median projections and 90% uncertainty intervals are depicted with black lines and grey shading.A grey dashed line marks the UNAIDS 2025 target of 95% viral suppression in those on ART.

Figure S6a .
Figure S6a.Modelled proportion of undiagnosed people living with HIV over time in Senegal from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex workers (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIVST scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.

Figure S6b .
Figure S6b.Modelled proportion of people treated for HIV over time in Senegal from 2000 to 2040 for a) all people living with HIV (PLHIV), b) all female sex workers (FSW) living with HIV (LHIV), c) all men who have sex with men (MSM) LHIV, and d) all clients of FSW LHIV.Median projections are depicted with black lines.The dashed lines display the counterfactual no HIVST scenario, the solid lines depict the ATLAS-only scenario, and the dotted lines represent the ATLAS-scale-up scenario.